A classification modeling approach for determining metabolite signatures in osteoarthritis

<div><p>Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.</p></div

A discrete lysoPC and PC signature of metabolites from males over the age of 50 was dominant in individuals over the age of 50 years and was indicative of males with OA versus HV.

<p>(A) Heat-map of the stratified cohort of individuals overs the age of 50 years separated by sex and total knee replacement due to osteoarthritis (OA) vs healthy adult volunteers (HV). (B & C) Venn diagrams generated by metabolite signatures (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199618#pone.0199618.t002" target="_blank">Table 2</a>) from males, females and all individuals over the age of 50 years (B) or males, individuals with body mass index (BMI) ≥ 30 or BMI < 30 kg/m² (C). (D & E) AUC curves generated by principal component with logistic regression (PCR) modeling using the metabolite signature (D) or aggregate sum of lysophosphatidylcholine (lysoPC), diacyl-phosphatidylcholine (PCaa) and acyl-alkylphosphatidylcholine (PCae) analogues (E) from the male age > 50 years stratified population. Blue lines represent training set area under the curve (AUC). Red lines represent test set AUC. Dotted lines are 95% confidence intervals.</p

Demographics of the Newfoundland cohort stratified groups consisting of healthy volunteers (HV) and patients undergoing total knee replacement for osteoarthritis (OA).

<p>Demographics of the Newfoundland cohort stratified groups consisting of healthy volunteers (HV) and patients undergoing total knee replacement for osteoarthritis (OA).</p

A stepwise approach to metabolite signature identification and predictive model optimization using stratified populations from a single cohort.

<p>The following stepwise approach includes data from the age > 50 years stratified population and is representative of results generated for each subpopulation. AUC, area under the curve; lysophosphatidylcholine (lysoPC); diacyl-phosphatidylcholine (PCaa); acyl-alkylphosphatidylcholine (PCae); partial least squares with logistic regression (PLS); principal component analysis with logistic regression (PCR).</p

Model area under the receiver-operator curve values (AUC) of the 2.5%, 50% and 97.5% quantiles generated from bootstrapped multivariate analysis of metabolites determined to be predictive from univariate analysis of stratified groups of study participants described in Table 1.

<p>Model area under the receiver-operator curve values (AUC) of the 2.5%, 50% and 97.5% quantiles generated from bootstrapped multivariate analysis of metabolites determined to be predictive from univariate analysis of stratified groups of study participants described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199618#pone.0199618.t001" target="_blank">Table 1</a>.</p